Nature of aortic annulus: Influence of annulus dynamic on the aortic valve hemodynamics.

Accurate imaging reports of the aortic valve indicate that the diameter of the aortic annulus changes regularly during a cardiac cycle. Most of these studies aim to demonstrate the proper method for estimating the aortic annulus diameter before performing TAVR surgery, revealing that the aortic annulus is dynamic and not constant throughout the cardiac cycle. This raises the question of how fixing the aortic annulus might affect valve function, which is a question that still needs to be addressed. Therefore, the present study seeks to address this question and elucidate the dynamic impact of the aortic annulus on aortic valve hemodynamics. Two computational models based on this hypothesis were created and solved, and then their results were compared. Both models are identical, except for the intrinsic dynamic nature of the aortic annulus. One model consists of the dynamic behavior, and the other simulates a fixed annulus, resembling the effect of a TAVR operation, SAVR, or any phenomenon that eliminates the dynamic nature of the annulus. Our research findings indicate that the dynamic nature of the annulus enhances blood flow (+2.7 %), increases mean velocity (+11.9) and kinetic energy density (+34 %), prolongs momentum retention during systole, stabilizes the flow jet at the end of systole, reduces the required pressure to keep the leaflets open (-40.9 % at 0.3s), and sustains ventricular pressure superiority (+9.4 %) over the aorta for a longer duration (+17.7 % of systole), a crucial factor in preventing backflow during aortic valve closure. Based on these results, more attention should be paid to the dynamic nature of the annulus.

Microplastic pollution in the Chapora River, Goa, Southwest India: spatial distribution and risk assessment.

The amount of information available on the microplastic (MP) contamination in Goa's riverine water systems is currently limited. The abundance, size, colour, and polymer composition of microplastics in Chapora River surface water were investigated in this study. MPs in Chapora River surface water ranged from 0.1 particles/L (station 13) to 0.47 particles/L (station 5). The mean (± standard deviation) MP concentration was 0.25 (± 0.13) particles/L. Fibre was the dominant shape (77.15%), followed by fragments (12.36%), films (9.36%), and foam (1.12%). Most MPs were found in the 0.1-0.3 mm size range, then in the 0.3-1 mm and 1-5 mm. The dominant type of polymer studied was polyethylene terephthalate (PET; 46%), followed by high-density polyethylene (HDPE; 14%), polypropylene (PP; 5%), and polystyrene (PS; 1%). The risk assessment study indicated high risk with respect to PHI, while PLI shows low risk in the area. The source of MPs was mostly anthropogenic in nature in the region. When compared with other tropical rivers, MP pollution was relatively lower in the Chapora River. Nevertheless, the baseline data will help the local administration take mitigation measures to reduce the impact of MP pollution in the region.

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data.

Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

A matter of choice: Understanding the interactions between epiphytic foraminifera and their seagrass host Halophila stipulacea.

In sub/tropical waters, benthic foraminifera are among the most abundant epiphytic organisms inhabiting seagrass meadows. This study explored the nature of the association between foraminifera and the tropical seagrass species H. stipulacea, aiming to determine whether these interactions are facilitative or random. For this, we performed a "choice" experiment, where foraminifera could colonize H. stipulacea plants or plastic "seagrasses" plants. At the end of the experiment, a microbiome analysis was performed to identify possible variances in the microbial community and diversity of the substrates. Results show that foraminifera prefer to colonize H. stipulacea, which had a higher abundance and diversity of foraminifera than plastic seagrass plants, which increased over time and with shoot age. Moreover, H. stipulacea leaves have higher epiphytic microbial community abundance and diversity. These results demonstrate that seagrass meadows are important hosts of the foraminifera community and suggest the potential facilitative effect of H. stipulacea on epiphytic foraminifera, which might be attributed to a greater diversity of the microbial community inhabiting H. stipulacea.

Enhancing Pixel Charging Efficiency by Optimizing Thin-Film Transistor Dimensions in Gate Driver Circuits for Active-Matrix Liquid Crystal Displays.

Flat panel displays are electronic displays that are thin and lightweight, making them ideal for use in a wide range of applications, from televisions and computer monitors to mobile devices and digital signage. The Thin-Film Transistor (TFT) layer is responsible for controlling the amount of light that passes through each pixel and is located behind the liquid crystal layer, enabling precise image control and high-quality display. As one of the important parameters to evaluate the display performance, the faster response time provides more frames in a second, which benefits many high-end applications, such as applications for playing games and watching movies. To further improve the response time, the single-pixel charging efficiency is investigated in this paper by optimizing the TFT dimensions in gate driver circuits in active-matrix liquid crystal displays. The accurate circuit simulation model is developed to minimize the signal's fall time (Tf) by optimizing the TFT width-to-length ratio. Our results show that using a driving TFT width of 6790 µm and a reset TFT width of 640 µm resulted in a minimum Tf of 2.6572 µs, corresponding to a maximum pixel charging ratio of 90.61275%. These findings demonstrate the effectiveness of our optimization strategy in enhancing pixel charging efficiency and improving display performance.

Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT.

Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperature are of critical importance in plant growth, and predicting them enables farmers to take preventive actions, thereby mitigating the issues affecting plant health. This work presents a plant health monitoring approach by forecasting soil moisture and heat levels by collecting data in an Internet of Things (IoT) environment. Here, for transmitting the soil data acquired by the IoT nodes, a cluster head (CH) selection and routing technique using Gannet Namib beetle optimization (GNBO) is used. The data is routed to a prediction module, wherein soil moisture and heat levels are predicted by Convolutional long short term memory (Conv-LSTM). Furthermore, the hyperparameters of the Conv-LSTM are optimized by the GNBO algorithm. The efficiency of the GNBO-Conv-LSTM is examined based on link life time (LLT), energy, delay, distance, negative predictive value (NPV), positive predictive value (PPV), and true negative rate (TNR) and is observed to have achieved values of 0.675, 0.478 J, 0.092 ms, 50.200 m, 0.885, 0.882, and 0.875, correspondingly.

Risk assessment of polycyclic aromatic hydrocarbon infested sediments along the coast of Goa, India.

Goa, a small state in India, is a tourist hotspot known for its coastline and beaches. Anthropogenic activities lead to pollution in the environment. Amongst many other pollutants, polycyclic aromatic hydrocarbons represent a major class of contaminants owing to their widespread distribution and protracted environmental persistence. No information is available about the levels of Polycyclic aromatic hydrocarbon (PAH) contamination in the sediments along the coastline of Goa. The study aimed to establish a baseline for PAH concentrations in the sediments along the shoreline to help comment on the pollution levels caused and thereby understand the risk and their impact on the marine life therein. The total concentration of PAHs along the selected sampling sites of the Goa coastline was 1.00 to 875 µg g-1. Maximum PAH concentrations were detected in the Divar island mangrove (875 µg g-1); the least was observed at Galgibaga beach (365 µg g-1). The results revealed that the sediment of the Goa coastline is heavily contaminated with PAH. Source apportionment of PAHs was analysed based on diagnostic ratios, and results exhibited that petroleum products and their combustion were primarily responsible for their generation. The results of risk quotients showed that the values are way above effect range median (ER-M), indicating these could pose a high risk to the ecosystem.

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

INTRODUCTION: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization (AJO) algorithm for minimizing the feature dimensionality. METHOD: The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation over the boundary regions of the brain tumor. RESULT: The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both BRATS and Kaggle datasets. CONCLUSION: Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.

Energy dispersive X-Ray microanalysis in conjunction with scanning electron micrography to establish nematodes as bioindicators in marine fish environment.

Energy Dispersive X-Ray Microanalysis has been used as the non-invasive technique on Indian helminthes to explore the role of nematode parasites as bioindicators in the marine ecosystem of Central West coast of India for the first time. The accumulation of sulphur and iron were analysed from a raphidascaridoid roundworm, Rostellascaris spinicaudatum (Malhotra and Anas) parasitizing marine catfish, Arius maculatus from the Central West coast of India at Goa. Quantitatively, the cuticle on oral armature comprised as much as ten times more sulphur than iron content in the roundworm under study. However, only carbon and oxygen were detected over caudal papillae, where no metals or other elements were recorded. The utility of a raphidascaridoid nematode to act as a bioindicator, that had the potential of a bioaccumulator effector, is highlighted.

Long-term durability of a new surgical aortic valve: A 1 billion cycle in vitro study.

Background: This study assessed the long-term hemodynamic functional performance of the new Inspiris Resilia aortic valve after accelerated wear testing (AWT). Methods: Three 21-mm and 23-mm Inspiris valves were used for the AWT procedure. After 1 billion cycles (equivalent to 25 years), the valves' hemodynamic performance was compared with that of the corresponding zero-cycled condition. Next, 1 AWT cycled valve of each valve size was selected at random for particle image velocimetry (PIV) and leaflet kinematic tests, and the data were compared with data for an uncycled Inspiris Resilia aortic valve of the same size. PIV was used to quantitatively evaluate flow fields downstream of the valve. Valves were tested according to International Standards Organization 5840-2:2015 protocols. Results: The 21-mm and 23-mm valves met the International Organization for Standardization (ISO) durability performance requirements to 1 billion cycles. The mean effective orifice areas for the 21-mm and 23-mm zero-cycled and 1 billion-cycled valves were 1.89 ± 0.02 cm2 and 1.94 ± 0.01 cm2, respectively (P < .05) and 2.3 ± 0.13 cm2 and 2.40 ± 0.11 cm2, respectively (P < .05). Flow characterization of the control valves and the study valves demonstrated similar flow characteristics. The velocity and shear stress fields were also similar in the control and study valves. Conclusions: The Inspiris Resilia aortic valve demonstrated very good durability and hemodynamic performance after an equivalent of 25 years of simulated in vitro accelerated wear. The study valves exceeded 1 billion cycles of simulated wear, 5 times longer than the standard requirement for a tissue valve as stipulated in ISO 5840-2:2015.

Deep neural learning based protein function prediction.

It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.

Spatio-temporal assessment of COVID-19 lockdown impact on beach litter status and composition in Goa, India.

The study aimed to understand beach litter status at some of the world-famous beaches of Goa, West India, to comprehend the impact of the Covid-19 lockdown. We characterize litter in six categories (Nylon+Rubber, Plastics, Footwear, Glass, Metal, and Thermocol) for eight sampled beaches in the north and south Goa. All beaches show increased glass and decreased plastics (significant litter) during the lockdown period compared to the unlock period that marked the high tourist inflow. Beaches were classified and graded with colour codes using litter density exhibit light blue-green colour coding during the lockdown or unlock period, suggests clean maintenance. The Miramar beach located in the heart of the capital city showed relatively more litter density (yellow code) due to the combination of local people and tourist inflow. Morjim, Palolem, Velsao were littered the least during both periods.

Saving collections: taxonomic revision of the herpetological collection of the Instituto de Investigação Científica Tropical, Lisbon (Portugal) with a protocol to rescue abandoned collections.

The herpetological collections of the Instituto de Investigação Científica Tropical, Lisbon, are amongst the most important collections from the former Portuguese territories in Africa and Asia. The collection comprises more than 5000 preserved specimens, including type specimens of nine taxa, Trachylepis adamastor, Trachypelis thomensis, Panaspis thomensis, Naja peroescobari, Dalophia angolensis, Hemidactylus nzingae, Boaedon fradei, Platysaurus maculatus maculatus, and Platysaurus maculatus lineicauda. The collection was abandoned in the early years of 2000s and was at risk of being lost. In this paper the entire collection is reviewed, a catalogue provided of the extant specimens, and a brief account of the history of herpetological research at IICT given. Details are also provided on the recovery of the collection and a protocol to rescue abandoned collections.

In Vitro Assessment of Probiotic Potential of Saccharomyces Cerevisiae DABRP5 Isolated from Bollo Batter, a Traditional Goan Fermented Food.

Bollo is a traditional Goan fermented food in which coarse wheat/wheat and finger millet is leavened with toddy. We here isolated 42 yeast strains from Bollo batter. Initial screening of the isolates with probiotic properties yielded four yeast isolates (DABRP1, DABRP2, DABRP5 and DABRP12). These isolates exhibited tolerance to high bile salt concentration and acidic pH and resistance to various antibiotics, which indicated their probiotic nature. All these yeast isolates were identified as Saccharomyces cerevisiae through D1D2-LSU-rDNA sequencing. These yeast isolates also showed higher percent hydrophobicity towards chloroform followed by n-hexadecane and o-xylene indicating their mucosal surface-adhesive property. To evaluate the safety of the isolates for them to be called as generally recognized as safe, the pathogenic behavior of the isolates determined through the in vitro hemolysis assay and evaluation of DNase and gelatinase activities. None of the isolates exhibited hemolysis or produced DNase or gelatinase and thus were considered potentially safe. In terms of beneficial effects, the most potent isolate S. cerevisiae DABRP5 showed antibacterial activity against the test pathogens. It also showed excellent antioxidant activity with DPPH free radical scavenging activity of 68.85 ± 0.69%, anti-inflammatory activity with 60.39 ± 0.34% inhibition of protein denaturation, and antidiabetic activity with 71.75 ± 0.45% inhibition of α-amylase activity. The isolate produced α-amylase, lipase, and ß-galactosidase. The probiotic potential of the isolate S. cerevisiae DABRP5 was similar to that of the reference strain (Saccharomyces boulardii CNCM I-745) used in this study. The results thus indicate that yeast isolates from Bollo batter have probiotic potential.

High-Sensitivity and Ultrafast-Response Ethanol Sensors Based on Graphene Oxide.

Ethanol sensors with ultrafast response and high sensitivity have attracted much attention to be applied to daily industrial production processes. In this work, graphene oxide-aniline (GOA) sensors are proposed to meet the requirements of detecting ethanol concentration. Graphene oxide is an outstanding material that has excellent electrical and thermal conductivity, large specific surface area, and high carrier mobility. Because of its special bonding reactions, GOA has advantages of good dispersibility, good electrical conductivity, insolubility in water, and strong plasticity. When testing ethanol concentration with sensors, there will be a lag time, which determines the sensitivity of the sensors. To the best of our knowledge, the GOA sensors in this work have the fastest response time, which is only 27 ms. The GOA ethanol sensors show a good ethanol sensing performance, including excellent sensitivity, cycle stability, and long-term stability.

An adaptive technique based blood glucose control in type-1 diabetes mellitus patients.

This study proposes Grasshopper Optimization Algorithm (GOA) based type 1 diabetes mellitus system utilizing the nonlinear Bergman minimal model with proportional integral derivative (PID) controller. GOA is the optimization algorithm, which is utilized for selecting the optimized tuning parameters of the PID controller also solves the nonlinear system parameter identification problem. The novelty of the proposed study is to stabilize the glucose level in blood for type 1 diabetic patients by infusion of insulin in reduced time with optimal quantity. Without any intervention to the normal activities of patients, the supply of insulin injection and glucose monitoring is performed automatically for type 1 diabetic patients using this controller. In between the measured variable and set point, the difference is calculated by the PID controller to evaluate an error values. In realistic patient oriented conditions, the control performance evaluation, control optimization, and advanced patient modelling should be highly concentrated during the research/analysis on blood glucose control. Evaluation is performed to analyze control performances and implementation is done on Simulink/MATLAB environment. The performance analysis of the type 1 diabetes mellitus system with GOA technique is also discussed and to improve the control performance, to optimize the controller parameters. The simulation results have proved the substantial improvement in the performance of proposed algorithm with the better results achieved than the other conventional controllers such as PSO-PID and EHO-PID.

The contribution of age and obesity to the number of painful joint sites in individuals reporting osteoarthritis: a population-based study.

OBJECTIVE: To investigate the association of OA risk factors with number of painful joint sites in a representative population sample. METHODS: Analysis of the 2009 Survey on Living with Chronic Diseases in Canada - Arthritis Component (n = 1614) for respondents reporting symptomatic OA. Variables: painful joints sites (hands, wrists, elbows, shoulders, hips, knees, ankles, feet, back, neck), joint symptom duration, sociodemographic characteristics, smoking, comorbidities and BMI. Zero-truncated negative binomial regressions were used to investigate the association between number of painful joint sites and the variables. Generalizability of findings was assessed by a similar analysis in a clinical hip/knee OA sample. RESULTS: The sample comprised 73% women and 56% were aged <65 years. The mean number of painful joint sites was 3.8: 84% reported pain at ≥2 sites, and 45% at ≥4 sites. Age, BMI, education and smoking were not associated with the number of joint sites. Significant associations were found with being female [rate ratio (RR) = 1.23, 95% CI 1.09, 1.39], having more comorbidities (RR = 1.11, 95% CI 1.07, 1.15) and longer symptom duration (RR = 1.16, 95% CI 1.09, 1.24), although the increase in joint sites with duration was small. Similar regression results were found with the clinical OA sample. CONCLUSION: The lack of an association of age and BMI (obesity) with number of painful joint sites in OA raises questions about the role of these risk factors and our understanding of OA as a multi-joint disease. Filling this knowledge gap is critical to making progress with defining OA phenotypes and identifying potential aetiological mechanisms.

Serological evidence of Japanese encephalitis virus infection in pigs in a low human incidence state, Goa, India.

Japanese encephalitis (JE) is a mosquito-borne zoonosis caused by Japanese encephalitis virus (JEV). It is a leading cause of encephalitis in humans especially children in Asia. Aquatic wading birds are the reservoirs and pigs serve as amplifying hosts for JEV. Humans and horses are dead-end hosts. JE is endemic in several states of India. Goa, a small state on the west coast of India, had witnessed JE outbreaks in the past and as on date human JE cases are reported sporadically. Although human JE cases are well documented in Goa, the status of JEV exposure of pigs has not been well documented. Hence the present study was undertaken with an objective of identifying JEV exposure in the pig population of Goa state in the light of declining human JE cases. To achieve the objective, between January 2017 and May 2019, serum samples from 666 pigs were screened using enzyme-linked immunosorbent assay (ELISA) for the detection of anti-JEV IgG. The apparent prevalence of anti-JEV IgG in pigs was found to be 7.1 % (95 % confidence interval 5.3 %-9.3 %) and true prevalence was 4.6 % (95 % confidence interval 2.7 %-7.1 %). The seroprevalence of JE recorded in pigs of Goa state was low compared to other endemic states in India, which may also be one of the reasons for the lower prevalence of human JE cases in Goa state. Univariate analysis revealed that the age of the pigs and district did not significantly influence the JE seroprevalence in pigs of Goa state. However, in multivariable logistic regression, the North Goa district was found to significantly (p = 0.017) influence the JE seroprevalence in pigs. The study identified that JEV is still circulating in the Goan pig population and hence constant vigil is required to monitor the intensity of JEV circulation in pigs. Besides forewarning possible human outbreaks in the locality, evidence of JEV exposure in pig population provides valuable data on the magnitude and extent of geographical spread.

Comprehensive diabetes care: The Goa model.

Diabetes mellitus continues to increase in epidemic proportions globally as well as in India. Poor glycemic control in long-standing diabetes mellitus eventually leads to chronic complications such as retinopathy, nephropathy, neuropathy, and cardiovascular disease. Diabetic retinopathy is emerging as an important cause of avoidable visual impairment and blindness in India across all strata of society. Much of this vision loss can be prevented by improving control of known risk factors, annual fundus screening, with prompt treatment of individuals with sight-threatening retinopathy. The Queen Elizabeth Diamond Jubilee Trust has made a significant contribution by supporting such a program across India, including Goa. The newly established medical retina clinic at Goa Medical College now provides facilities for screening, a detailed evaluation of advanced retinopathy, and therapeutic modalities such as laser and intravitreal injections. The peripheral centers are equipped to screen all people with diabetes mellitus and refer those with sight-threatening retinopathy to the medical college. The provision of a foot scanner to evaluate the risk of foot ulcers and microalbuminuria assessment as part of the nephropathy screening would encompass the entire gamut of diabetic microvascular complications. The next decade would provide evidence if this initiative, with the enthusiastic partnership of the state government, results in reduction of blindness in the people of Goa and an overall reduction in diabetes-related morbidity and mortality.

Spatial and temporal variability in concentration and characteristics of aerosols at Visakhapatnam (east) and Goa (west) coasts of India.

Knowledge on the variability in quantity and compositions of various size groups of aerosols is important to understand their sources and their role in biogeochemical and climate processes. Here, we studied total suspended particles (TSP), PM10 and PM2.5 for their quantitative and water soluble compositional (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Ca2+, and Mg2+) distributions, and to understand their nature and potential sources at Goa and Visakhapatnam on the west and east coasts, respectively, of India. While the mean concentrations of TSP were found to be 117 ± 44 and 85 ± 51 µg/m3 its maximal levels occurred in spring intermonsoon (SIM; 141 ± 52) and winter monsoon (WM; 155 ± 145 µg/m3) seasons at Goa and Visakhapatnam, respectively. PM10 and PM2.5 exhibited higher ranges at Visakhapatnam than Goa. The increase in PM2.5 abundance from WM to SIM at Visakhapatnam seems to occur in coincidence with decrease in TSP favored by topography and ambient meteorological conditions. Locally released and seasonally transported (from land and sea) constituents contributed to the observed variability in aerosol compositions. Sulphate dominated the aerosol composition at both Goa (57-64%) and Visakhapatnam (43-55%) followed by NO3- (5-16% and 6-18%, respectively) where the former component was higher in PM10 and PM2.5. The NO3- was more in TSP. Relations between SO42- and NH4+ suggested possible presence of NH4HSO4. Examination of ionic ratios and balance suggested near neutrality in PM10 and PM2.5 while TSP was acidic at the both locations. Notable relations between Ca2+ and NO3-, particularly in PM10 at Goa, indicated their release from mining related activities.